Factors Influencing Consumers’ Intention to Adopt Fashion Robot Advisors: Psychological Network Analysis

Abstract

Drawing upon the theory of human–robot interaction (HRI), this study examined the relations among perceived characteristics of fashion robot advisors (FRAs), consumers’ negative preconceptions toward robots, and positive dispositions toward technology to identify network differences in adoption and nonadoption groups. For interviews, pretests, and main data collection, we presented video clips of FRAs as stimuli. Based on the data (n = 464) collected via an online survey, we conducted psychological network analysis to explore defining factors that differentiate adoption and nonadoption groups. The results indicate that perceived characteristics of social intelligence, humanlikeness, and knowledgeableness combined with a positive disposition of technological self-efficacy lead to adoption of FRAs. This study contributes to the literature on the theory of HRI and technology acceptance models, particularly in fashion retail sectors. Furthermore, this study provides a new graphical approach to networks that conceptualizes shoppers’ adoption of technology as a complex interplay of psychological attributes.